I am unable to identify general temrs or specific source of information for the below proposed problem. I would appreciate if the community can guide me to journal articles/books and keywords to look for in literature.
There is a non-linear dynamic system taking input and producing 1D time series as output. I would like to use NN to find parameters of the dynamic system, according to the time series output. That is, mapping the features of the time series (after transformation, likely Fourtier Transform or Wavelet) to the parameters governing the dynamics of the system.
Research so far:
I have found a few journal papers mostly processing sounds of rolling bearings or hearbeat but only for error/failure classification.
- Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and SOund Signals
- Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings
- Deep Learning Based Approach for Bearing Fault Diagnosis
- Detecting atrila fibrillation be deep convolutional neural networks
(the above are classification problems, my problem is about parameter identification)
Reason to address this on StackExchange:
I think I am missing overview about the topic (identification of dynamic systems using NN), because I am not able to reach more profound information. Also, I think that NN would be more beneficial to my current application than lets say optimization by evolutionary algorithms, threfore I am specifically asking for NN.